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Multi-Objective Evolutionary Algorithms: Past, Present, and Future

Part of the Springer Optimization and Its Applications book series (SOIA,volume 170)

Abstract

Evolutionary algorithms have become a popular choice for solving highly complex multi-objective optimization problems in recent years. Multi-objective evolutionary algorithms were originally proposed in the mid-1980s, but it was until the mid-1990s when they started to attract interest from researchers. Today, we have a wide variety of algorithms, and research in this area has become highly specialized. This chapter attempts to provide a general overview of multi-objective evolutionary algorithms, starting from their early origins, then moving in chronological order towards some of the most recent algorithmic developments. In the last part of the chapter, some future research paths on this topic are briefly discussed.

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Notes

  1. 1.

    The first author maintains the EMOO repository, which currently contains over 12,400 bibliographic references related to evolutionary multi-objective optimization. The EMOO repository is located at: https://emoo.cs.cinvestav.mx.

  2. 2.

    Without loss of generality, we will assume only minimization problems.

  3. 3.

    It is worth indicating that indicator-based archiving was introduced earlier (see [78, 79]).

  4. 4.

    See:http://ls11-www.cs.uni-dortmund.de/rudolph/hypervolume/start http://people.mpi-inf.mpg.de/~tfried/HYP/ http://iridia.ulb.ac.be/~manuel/hypervolume

  5. 5.

    NSGA-III was designed to solve many-objective optimization problems and its use is relatively popular today.

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Acknowledgements

The first author acknowledges support from CONACyT project no. 2016-01-1920 (Investigación en Fronteras de la Ciencia 2016) and from a project from the 2018 SEP-Cinvestav Fund (application no. 4).

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Coello, C.A.C., Brambila, S.G., Gamboa, J.F., Tapia, M.G.C. (2021). Multi-Objective Evolutionary Algorithms: Past, Present, and Future. In: Pardalos, P.M., Rasskazova, V., Vrahatis, M.N. (eds) Black Box Optimization, Machine Learning, and No-Free Lunch Theorems. Springer Optimization and Its Applications, vol 170. Springer, Cham. https://doi.org/10.1007/978-3-030-66515-9_5

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